1. Technical Field
The present disclosure relates to systems and methods for automatic bone detection in MRI images.
2. Discussion of Related Art
In recent years, medical imaging has experienced an explosive growth due to advances in imaging modalities such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound. MRI has demonstrated a high potential in bone metastasis identification at an early stage of growth. While MRI offers excellent tissue discrimination and can provide diagnostic quality images, automatic segmentation and classification of tissues in MRI images is difficult due to intra- and inter-scan intensity inhomogeneities. In addition to the high intensity variability within a single tissue type, in MRI images, several tissue types appear to have similar intensity and textural properties, making it harder to design automatic segmentation tools. For example the bone intensity and texture looks very similar to many soft tissues.
Bone consists of cortical or compact bone, trabecular bone and marrow. The compact bone surrounds trabecular bone (also called cancellous or spongy bone) which surrounds the marrow cavity. Compact bone forms the thick-walled shaft of long bones, such as the femur, tibia, fibula, ulna, radius and humerus. A thin layer of compact bone also covers the ends of long bones. Periosteum, which is a dense fibrous membrane that serves as an attachment for tendons and muscles, covers the surface of bones, except at their extremities. MRI can provide detailed images of the bone and bone marrow and is at least as sensitive as CT or X-rays for detecting bone marrow metastases. T1- or T2-weighted STIR (short tau-inversion recovery) images can be a component of an MR exam.
Although bone marrow lesions may be more accurately assessed by MRI than by other imaging modalities, MRI is less effective than X-rays or CT for detecting destruction of bone structure, because cortical bone does not produce a signal and appears black on T1- and T2-weighted sequences.